This article examines the evolution of computational natural philosophy, tracing its origins from the mathematical foundations of ancient natural philosophy, through Leibniz's concept of a “Calculus Ratiocinator,” to Turing's fundamental contributions in computational models of learning and the Turing Test for artificial intelligence. The discussion extends to the contemporary emergence of ChatGPT. Modern computational natural philosophy conceptualizes the universe in terms of information and computation, establishing a framework for the study of cognition and intelligence. Despite some critiques, this computational perspective has significantly influenced our understanding of the natural world, leading to the development of AI systems like ChatGPT based on deep neural networks. Advancements in this domain have been facilitated by interdisciplinary research, integrating knowledge from multiple fields to simulate complex systems. Large Language Models (LLMs), such as ChatGPT, represent this approach's capabilities, utilizing reinforcement learning with human feedback (RLHF). Current research initiatives aim to integrate neural networks with symbolic computing, introducing a new generation of hybrid computational models. While there remain gaps in AI's replication of human cognitive processes, the achievements of advanced LLMs, like GPT4, support the computational philosophy of nature—where all nature, including the human mind, can be described, on some level of description, as a result of natural computational processes.